CONCLUSIONS AND FUTURE WORK

7.1 Conclusions

Qualified transformer diagnostic works to maintain its electrical and mechanical integrity are among the highest priorities for utility stakeholders. Over the past decades, different transformer condition testing techniques have been developed, among which a transfer function method, namely, the Frequency Response Analysis (FRA), is known as the most accurate, widely accepted and the least invasive method. Other than that, FRA is capable of detecting a wide range of transformer internal faults, such as core problems, connection failure, winding structure deviations, etc. Despite the fact that FRA is quite a mature method with well-developed standards and solid background, understanding and interpretation of the FRA results requires further research and improvement. Therefore, different advanced techniques borrowed from machine learning, statistical analysis, image processing, and system modelling are being actively integrated with FRA results interpretation.

Transformer diagnostic state-of-the-art techniques, major internal and external fault

triggering factors, FRA concept, measurement setup configurations, standards and

interpretation techniques were discussed. Advantages and challenges of conventional and

advanced interpretation methods were emphasized. A new approach combining machine

learning tools and statistical analysis was introduced in this research. In particular,

bolstered error estimation and bootstrap sampling techniques were used to classify the

working condition of the transformer under the test, while 12 statistical indicators were

utilized as the classification criteria. The critical values of indices representing decision

boundaries were obtained via emulation of the winding short-circuit fault, where the

variable resistance rheostat was installed in parallel with winding terminals. The

established green-to-yellow (healthy-to-suspicious) and yellow-to-red (suspicious-to-

critical) decision boundaries were applied to different severity levels of the winding short-

circuit and mechanical deformation. Other than that, proposed interpretation method was

assessed on the winding software RLC model based on the fabricated air-core winding.

The obtained classification results were compared with the conventional methods available in the literature and standards.

Machine learning and statistical-based real-life diagnosis and forecasting will, in turn, facilitate isolation of the transformer unit from the grid before it damages other equipment, ignites or even causes major power outages. Hence, expenses for repair works, component replacement, and work injury compensation will be significantly reduced. On the other hand, determination of the residual service lifespan and severity of the damage of the transformer is vital for planning maintenance and repair budget. This in turn will decrease an inexpedient use of financial resources and a cash outflow.

7.2 Future work

Based on the theoretical study and practical results, there are several topics that can be studied in the future.

7.2.1 On-line FRA

It is obviously beneficial to implement a continuous on-line diagnosis system, which will alert the grid operator about the changes in the transformer structure. This will, in turn, prevent the emergency situation on the power network and severe damage of components, which might lead to severe economic and environmental losses. The on-line FRA test will also eliminate the necessity to de-energize transformer from the grid.

Nevertheless, implementation of the on-line FRA requires further research to resolve a number of challenges such as injection of the sweep signal, measuring the response signal, compensation circuit, mitigating the impact of external equipment and high voltage grid lines, etc. Stability of the power grid and loading should also be taken into consideration during on-line FRA measurements.

On the other hand, the merger of the real-time on-line FRA with the Supervisory

Control and Data Acquisition (SCADA) network of the grid will give the opportunity to

remotely isolate faulty transformer or reroute the power flow, hence reducing the necessity

for direct operational and maintenance activities, which in turn will raise the safety of

personnel and equipment. However, since electricity network is one of the most

strategically important facilities, the demand for cyber security of smart grid will most likely increase.

7.2.2 Three-phase winding RLC model

In this research work, a single-phase air-core winding was fabricated and its

software model was created in SPICE environment. The detailed RLC model was, as the

name suggest, based on the distributed parameters of the winding – resistance, inductance,

and capacitance. The mutual coupling between turns and adjacent disks were taken into

account by an analytical model. The winding software model was validated via practical

FRA measurements of the physical winding and further used for axial and radial winding

deformation. As the next step, it is reasonable to extend the created RLC model up to three-

phase winding with the magnetic core. Obviously, the model will become significantly

complicated in this case because the number of the distributed elements will dramatically

increase and additional element in terms of the mutual coupling between core and the

winding structure should be taken into consideration. Nevertheless, a comprehensive

model of the two-winding single-phase or three-phase transformer will facilitate a detailed

investigation of the winding mechanical deformation and displacement without having to

physically implement the irreversible fault.